The present invention relates generally to simulations of electronic circuits, and more particularly, to methods and systems for performing sensitivity analysis of simulations of semiconductor circuits.
Silicon design technology is approaching 65 nanometer and ever smaller feature size. The number of transistors is growing exponentially as the feature size gets smaller. As a result there is a growing demand in high performance electronic design automation (EDA) software tools that can perform the ultra-large size transistor level electrical circuit verification in the reasonable amount of time. One of the most challenging issues coming with 65 nm and smaller feature sizes is accounting for on-chip process parameter variations to predict the entire system yield and manufacturability. Process parameter variations result in variations in device parameters (e.g., width, length, depth, etc.) that are impact performance of the device.
The promising approach to address the ultra-large size transistor level electrical circuit verification in the reasonable amount of time uses a statistical analysis. However, statistical analysis has been applied mostly in the timing analysis area where direct operations on probability functions are available. Statistical circuit simulation traditionally provides more accurate results at the expense of performance. Statistical simulation is normally done using a well-known Monte-Carlo Analysis which can be applied to the systems of limited complexity due to its runtime constraints.
A promising approach is statistical simulation based on the sensitivity analysis. With process parameters partially correlated this type of analysis usually gives accurate results with performance significantly improved with respect to Monte-Carlo analysis. However the typical combination of statistical simulation and Monte-Carlo analysis yields performance that is still not acceptable for very large scale circuits and the netlists (i.e., the interconnections and circuit and device descriptions) because the resulting process is too complex and too slow to run in simulation. In view of the foregoing, there is a need for an improved, pared down combination of statistical simulation and Monte-Carlo analysis.